% Created 2020-09-22 mar. 09:51
% Intended LaTeX compiler: pdflatex
\documentclass[conference]{IEEEtran}
\usepackage[utf8]{inputenc}
\usepackage[T1]{fontenc}
\usepackage{graphicx}
\usepackage{grffile}
\usepackage{longtable}
\usepackage{wrapfig}
\usepackage{rotating}
\usepackage[normalem]{ulem}
\usepackage{amsmath}
\usepackage{textcomp}
\usepackage{amssymb}
\usepackage{capt-of}
\usepackage{hyperref}
\usepackage[most]{tcolorbox}
\usepackage{bm}
\usepackage{booktabs}
\usepackage{tabularx}
\usepackage{array}
\usepackage{siunitx}
\IEEEoverridecommandlockouts
\usepackage{cite}
\usepackage{amsmath,amssymb,amsfonts}
\usepackage{algorithmic}
\usepackage{graphicx}
\usepackage{textcomp}
\usepackage{xcolor}
\usepackage{cases}
\usepackage{tabularx,siunitx,booktabs}
\usepackage{algorithmic}
\usepackage{import, hyperref}
\renewcommand{\citedash}{--}
\def\BibTeX{{\rm B\kern-.05em{\sc i\kern-.025em b}\kern-.08em T\kern-.1667em\lower.7ex\hbox{E}\kern-.125emX}}
\usepackage{showframe}
\author{\IEEEauthorblockN{Dehaeze Thomas} \IEEEauthorblockA{\textit{European Synchrotron Radiation Facility} \\ Grenoble, France\\ \textit{Precision Mechatronics Laboratory} \\ \textit{University of Liege}, Belgium \\ thomas.dehaeze@esrf.fr }\and \IEEEauthorblockN{Collette Christophe} \IEEEauthorblockA{\textit{BEAMS Department}\\ \textit{Free University of Brussels}, Belgium\\ \textit{Precision Mechatronics Laboratory} \\ \textit{University of Liege}, Belgium \\ ccollett@ulb.ac.be }}
\date{2020-09-22}
\title{Robust and Optimal Sensor Fusion}
\begin{document}

\maketitle

\begin{abstract}
Abstract text to be done
\end{abstract}

\begin{IEEEkeywords}
Complementary Filters, Sensor Fusion, H-Infinity Synthesis
\end{IEEEkeywords}

\section{Introduction}
\label{sec:org2c6d9ef}
\label{sec:introduction}

\section{Optimal Super Sensor Noise: \(\mathcal{H}_2\) Synthesis}
\label{sec:org5aa0717}
\label{sec:optimal_fusion}

\subsection{Sensor Model}
\label{sec:org8f1053d}

\subsection{Sensor Fusion Architecture}
\label{sec:orgc40deb4}

\begin{figure}[htbp]
\centering
\includegraphics[scale=1]{figs/sensor_fusion_noise_arch.pdf}
\caption{\label{fig:sensor_fusion_noise_arch}Figure caption}
\end{figure}

Let note \(\Phi\) the PSD.
\(\tilde{n}_1\) and \(\tilde{n}_2\) are white noise with unitary power spectral density:
\begin{equation}
  \Phi_{\tilde{n}_i}(\omega) = 1
\end{equation}

\begin{equation}
  \hat{x} = \left( H_1 \hat{G}_1^{-1} G_1 + H_2 \hat{G}_2^{-1} G_2 \right) x + \left( H_1 \hat{G}_1^{-1} N_1 \right) \tilde{n}_1 + \left( H_2 \hat{G}_2^{-1} N_2 \right) \tilde{n}_2
\end{equation}

Suppose the sensor dynamical model \(\hat{G}_i\) is perfect:
\begin{equation}
  \hat{G}_i = G_i
\end{equation}

Complementary Filters
\begin{equation}
  H_1(s) + H_2(s) = 1
\end{equation}


\begin{equation}
  \hat{x} = x + \left( H_1 \hat{G}_1^{-1} N_1 \right) \tilde{n}_1 + \left( H_2 \hat{G}_2^{-1} N_2 \right) \tilde{n}_2
\end{equation}

Perfect dynamics + filter noise


\subsection{Super Sensor Noise}
\label{sec:orgf4b6ca9}

Let's note \(n\) the super sensor noise.

Its PSD is determined by:
\begin{equation}
  \Phi_n(\omega) = \left| H_1 \hat{G}_1^{-1} N_1 \right|^2 + \left| H_2 \hat{G}_2^{-1} N_2 \right|^2
\end{equation}

\subsection{\(\mathcal{H}_2\) Synthesis of Complementary Filters}
\label{sec:org5773772}

The goal is to design \(H_1(s)\) and \(H_2(s)\) such that the effect of the noise sources \(\tilde{n}_1\) and \(\tilde{n}_2\) has the smallest possible effect on the noise  \(n\) of the estimation \(\hat{x}\).

And the goal is the minimize the Root Mean Square (RMS) value of \(n\):
\begin{equation}
\label{eq:rms_value_estimation}
  \sigma_{n} = \sqrt{\int_0^\infty \Phi_{\hat{n}}(\omega) d\omega} = \left\| \begin{matrix} \hat{G}_1^{-1} N_1 H_1 \\ \hat{G}_2^{-1} N_2 H_2 \end{matrix} \right\|_2
\end{equation}

Thus, the goal is to design \(H_1(s)\) and \(H_2(s)\) such that \(H_1(s) + H_2(s) = 1\) and such that \(\left\| \begin{matrix} \hat{G}_1^{-1} N_1 H_1 \\ \hat{G}_2^{-1} N_2 H_2 \end{matrix} \right\|_2\) is minimized.

\begin{equation}
\begin{pmatrix}
  z_1 \\ z_2 \\ v
\end{pmatrix} = \begin{bmatrix}
  \hat{G}_1^{-1} N_1 & -\hat{G}_1^{-1} N_1 \\
  0                  &  \hat{G}_2^{-1} N_2 \\
  1                  &  0
\end{bmatrix} \begin{pmatrix}
  w \\ u
\end{pmatrix}
\end{equation}

The \(\mathcal{H}_2\) synthesis of the complementary filters thus minimized the RMS value of the super sensor noise.

\begin{figure}[htbp]
\centering
\includegraphics[scale=1]{figs/h_two_optimal_fusion.pdf}
\caption{\label{fig:h_two_optimal_fusion}Figure caption}
\end{figure}

\subsection{Example}
\label{sec:orged06a27}

\subsection{Robustness Problem}
\label{sec:org62b375f}

\section{Robust Sensor Fusion: \(\mathcal{H}_\infty\) Synthesis}
\label{sec:orgef03e7c}
\label{sec:robust_fusion}

\subsection{Representation of Sensor Dynamical Uncertainty}
\label{sec:org9c9762b}

\subsection{Sensor Fusion Architecture}
\label{sec:org9572e70}
\begin{equation}
  \hat{x} = \left( H_1 \hat{G}_1^{-1} (1 + w_1 \Delta_1) G_1 + H_2 \hat{G}_2^{-1} (1 + w_2 \Delta_2) G_2 \right) x
\end{equation}
with \(\Delta_i\) is any transfer function satisfying \(\| \Delta_i \|_\infty < 1\).

Suppose the model inversion is equal to the nominal model:
\begin{equation}
  \hat{G}_i = G_i
\end{equation}

\begin{equation}
  \hat{x} = \left( 1 + H_1 w_1 \Delta_1 + H_2 w_2 \Delta_2 \right) x
\end{equation}

\begin{figure}[htbp]
\centering
\includegraphics[scale=1]{figs/sensor_fusion_arch_uncertainty.pdf}
\caption{\label{fig:sensor_fusion_arch_uncertainty}Figure caption}
\end{figure}

\subsection{Super Sensor Dynamical Uncertainty}
\label{sec:orgb9ee83e}

The uncertainty set of the transfer function from \(\hat{x}\) to \(x\) at frequency \(\omega\) is bounded in the complex plane by a circle centered on 1 and with a radius equal to \(|w_1(j\omega) H_1(j\omega)| + |w_2(j\omega) H_2(j\omega)|\).

\begin{figure}[htbp]
\centering
\includegraphics[scale=1]{figs/uncertainty_set_super_sensor.pdf}
\caption{\label{fig:uncertainty_set_super_sensor}Figure caption}
\end{figure}

\subsection{\(\mathcal{H_\infty}\) Synthesis of Complementary Filters}
\label{sec:orgf4e3c8e}

In order to minimize the super sensor dynamical uncertainty

\begin{figure}[htbp]
\centering
\includegraphics[scale=1]{figs/h_infinity_robust_fusion.pdf}
\caption{\label{fig:h_infinity_robust_fusion}Figure caption}
\end{figure}

\subsection{Example}
\label{sec:org4f663bc}

\section{Optimal and Robust Sensor Fusion: Mixed \(\mathcal{H}_2/\mathcal{H}_\infty\) Synthesis}
\label{sec:org150b612}
\label{sec:optimal_robust_fusion}

\subsection{Sensor Fusion Architecture}
\label{sec:org9bc69b7}

\begin{figure}[htbp]
\centering
\includegraphics[scale=1]{figs/sensor_fusion_arch_full.pdf}
\caption{\label{fig:sensor_fusion_arch_full}Figure caption}
\end{figure}

\subsection{Synthesis Objective}
\label{sec:orgbc5ac30}

\subsection{Mixed \(\mathcal{H}_2/\mathcal{H}_\infty\) Synthesis}
\label{sec:org541ef02}

\begin{figure}[htbp]
\centering
\includegraphics[scale=1]{figs/mixed_h2_hinf_synthesis.pdf}
\caption{\label{fig:mixed_h2_hinf_synthesis}Figure caption}
\end{figure}

\subsection{Example}
\label{sec:org046c2e2}

\section{Experimental Validation}
\label{sec:org1bb9cff}
\label{sec:experimental_validation}

\subsection{Experimental Setup}
\label{sec:org2c63393}

\subsection{Sensor Noise and Dynamical Uncertainty}
\label{sec:orgb0c6496}

\subsection{Mixed \(\mathcal{H}_2/\mathcal{H}_\infty\) Synthesis}
\label{sec:orgfb3986f}

\subsection{Super Sensor Noise and Dynamical Uncertainty}
\label{sec:orgfd5c11e}

\section{Conclusion}
\label{sec:orgda418fa}
\label{sec:conclusion}

\section{Acknowledgment}
\label{sec:orgabdae67}

\bibliography{ref}
\end{document}
